Methods for analyte monitoring management and analyte measurement data management, and articles of manufacture related thereto

ABSTRACT

Generally, methods of analyte monitoring management, and articles of manufacturing related thereto, are provided. The methods include receiving analyte measurement data and analyzing the analyte measurement data for health related parameters. Recommendations are determined for creating or modifying a treatment program based on the analysis, and provided within a user-interface that enables a user to create or modify the treatment program. Further, generally, methods of for managing analyte measurement data, and articles of manufacturing related thereto, are provided. The methods include receiving analyte measurement data that represent data collected over a time period, and analyzing the analyte measurement data for analyte episodes within that time period. Threshold based episodes and/or rate-of-change based episodes may be determined.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.13/629,262, filed Sep. 27, 2012, which claims priority based to U.S.Provisional Application No. 61/540,332, filed Sep. 28, 2011, thedisclosures of which are incorporated by reference herein in theirentireties for all purposes.

INTRODUCTION

Diabetes patients regularly consult with their health care practitioner(HCP) in order to assess the progress of their diabetes management, andto evaluate areas in need for improvement. The patient'sresponsibilities may include keeping diligent record of relevantinformation such as meal times and amount, fasting periods, insulinintake, exercise, and glucose measurements.

When patients return for a visit to the HCP, the HCP may collect theglucose measurements and acquire information from the collection ofmeasurements. Information acquired from the measurements by existingsoftware typically focus on overall summary statistics, such as medianand percent or measurements in a target range. The software provides ageneral picture of the glucose measurements as a whole and does notfocus in on more specific episodes within the measurements that may beuseful in treatment determination or modification. Such general summaryinformation is limited and more clinically meaningful health relatedinformation is lacking.

SUMMARY

In some aspects of the present disclosure, methods for analytemonitoring management are provided. The methods include receivinganalyte measurement data and analyzing the analyte measurement data forhealth related parameters. The analyte measurement data representsanalyte measurement data collected over a time period. The methods alsoinclude determining recommendations for creating or modifying atreatment program based on the analysis. The recommendations modulatethe health related parameters to improve one or more of the healthrelated parameters. The methods include generating a user-interface toenable a user to create or modify the treatment program. The userinterface provides the recommendations to guide the user in creating ormodifying the treatment program. The recommendations are optional andnot required to be implemented by the user. The methods also includeconfiguring an analyte monitoring device according to the created ormodified treatment program. In some aspects of the present disclosure,articles of manufacture are provided that include a machine-readablemedium having machine-executable instructions stored thereon for analytemonitoring management according to the methods described above.

In some aspects of the present disclosure, methods for managing analytemeasurement data are provided. The methods include receiving analytemeasurement data that represent data collected over a time period, andanalyzing the analyte measurement data for analyte episodes within thattime period. The analyte episodes include at least one threshold basedepisode. The threshold based episode is based on measurements meeting anentrance threshold for entering the threshold based episode. Further,the threshold based episode requires at least one of: a minimum numberof measurements meeting the entrance threshold; a minimum duration oftime meeting the entrance threshold; and a minimum area for measurementsmeeting the entrance threshold. The methods also include storing theanalyte episodes in memory. In some aspects of the present disclosure,articles of manufacture are provided that include a machine-readablemedium having machine-executable instructions stored thereon formanaging analyte measurement data according to the methods describedabove.

In some aspects of the present disclosure, methods for managing analytemeasurement data are provided. The methods include receiving analytemeasurement data that represent data collected over a time period, andanalyzing the analyte measurement data for analyte episodes within thattime period. The analyte episodes include at least one rate-of-changebased episode. The rate-of-change based episode requires a core of theepisode to meet a threshold rate for a duration threshold. The methodsalso include storing the analyte episodes in memory. In some aspects ofthe present disclosure, articles of manufacture are provided thatinclude a machine-readable medium having machine-executable instructionsstored thereon for managing analyte measurement data according to themethods described above.

INCORPORATION BY REFERENCE

The following patents, applications and/or publications are incorporatedherein by reference for all purposes: U.S. Pat. Nos. 7,041,468;5,356,786; 6,175,752; 6,560,471; 5,262,035; 6,881,551; 6,121,009;7,167,818; 6,270,455; 6,161,095; 5,918,603; 6,144,837; 5,601,435;5,822,715; 5,899,855; 6,071,391; 6,120,676; 6,143,164; 6,299,757;6,338,790; 6,377,894; 6,600,997; 6,773,671; 6,514,460; 6,592,745;5,628,890; 5,820,551; 6,736,957; 4,545,382; 4,711,245; 5,509,410;6,540,891; 6,730,200; 6,764,581; 6,299,757; 6,461,496; 6,503,381;6,591,125; 6,616,819; 6,618,934; 6,676,816; 6,749,740; 6,893,545;6,942,518; 6,514,718; 5,264,014; 5,262,305; 5,320,715; 5,593,852;6,746,582; 6,284,478; 7,299,082; U.S. Provisional Application No.61/149,639, entitled “Compact On-Body Physiological Monitoring Deviceand Methods Thereof”, U.S. patent application Ser. No. 11/461,725, filedAug. 1, 2006, entitled “Analyte Sensors and Methods”; U.S. patentapplication Ser. No. 12/495,709, filed Jun. 30, 2009, entitled “ExtrudedElectrode Structures and Methods of Using Same”; U.S. Patent ApplicationPublication No. 2004/0186365; U.S. Patent Application Publication No.2007/0095661; U.S. Patent Application Publication No. 2006/0091006; U.S.Patent Application Publication No. 2006/0025662; U.S. Patent ApplicationPublication No. 2008/0267823; U.S. Patent Application Publication No.2007/0108048; U.S. Patent Application Publication No. 2008/0102441; U.S.Patent Application Publication No. 2008/0066305; U.S. Patent ApplicationPublication No. 2007/0199818; U.S. Patent Application Publication No.2008/0148873; U.S. Patent Application Publication No. 2007/0068807; U.S.Patent Application Publication No. 2010/0198034; and U.S. ProvisionalApplication No. 61/149,639 titled “Compact On-Body PhysiologicalMonitoring Device and Methods Thereof”, the disclosures of each of whichare incorporated herein by reference in their entireties.

BRIEF DESCRIPTION OF THE DRAWINGS

A detailed description of various embodiments of the present disclosureis provided herein with reference to the accompanying drawings, whichare briefly described below. The drawings are illustrative and are notnecessarily drawn to scale. The drawings illustrate various embodimentsof the present disclosure and may illustrate one or more embodiment(s)or example(s) of the present disclosure in whole or in part. A referencenumeral, letter, and/or symbol that is used in one drawing to refer to aparticular element may be used in another drawing to refer to a likeelement.

FIG. 1 illustrates a flowchart of a method for analyte (e.g., glucose)monitoring management, according to one embodiment.

FIG. 2A illustrates a flowchart for a method of managing analytemeasurement data, according to one embodiment.

FIG. 2B illustrates a flowchart for a method of managing analytemeasurement data, according to one embodiment.

FIG. 3 illustrates a flowchart of a method for managing analyte (e.g.,glucose) measurements that includes analyzing measurement data forthreshold based episodes, according to one embodiment.

FIG. 4 illustrates a hypoglycemic episode for a set of glucosemeasurement data, according to one embodiment.

FIG. 5 illustrates a hyperglycemic episode for a set of glucosemeasurement data, according to one embodiment.

FIG. 6 illustrates a flowchart of a method for managing analyte (e.g.,glucose) measurements that includes analyzing measurement data forrate-of-change based episodes, according to one embodiment.

FIG. 7 illustrates an example of a detection of a glucose rise episodein a set of glucose measurement data, according to one embodiment.

FIG. 8 illustrates an example of a detection of a glucose fall episodein a set of glucose measurement data, according to one embodiment.

FIG. 9 illustrates a data processing device that may perform the methodsdescribed herein, according to one embodiment.

FIGS. 10-30 illustrate the user interface that is generated to enable auser to create or modify a treatment program, and further provides therecommendations to guide the user in creating or modifying the treatmentprogram.

DETAILED DESCRIPTION

Before the embodiments of the present disclosure are described, it is tobe understood that the present disclosure is not limited to particularembodiments described, as such may, of course, vary. It is also to beunderstood that the terminology used herein is for the purpose ofdescribing particular embodiments only, and is not intended to belimiting, since the scope of the embodiments of the present disclosurewill be limited only by the appended claims.

Where a range of values is provided, it is understood that eachintervening value, to the tenth of the unit of the lower limit unlessthe context clearly dictates otherwise, between the upper and lowerlimits of that range is also specifically disclosed. Each smaller rangebetween any stated value or intervening value in a stated range and anyother stated or intervening value in that stated range is encompassedwithin the present disclosure. The upper and lower limits of thesesmaller ranges may independently be included or excluded in the range,and each range where either, neither or both limits are included in thesmaller ranges is also encompassed within the present disclosure,subject to any specifically excluded limit in the stated range. Wherethe stated range includes one or both of the limits, ranges excludingeither or both of those included limits are also included in the presentdisclosure.

In the description of the present disclosure herein, it will beunderstood that a word appearing in the singular encompasses its pluralcounterpart, and a word appearing in the plural encompasses its singularcounterpart, unless implicitly or explicitly understood or statedotherwise. Merely by way of example, reference to “an” or “the”“analyte” encompasses a single analyte, as well as a combination and/ormixture of two or more different analytes, reference to “a” or “the”“concentration value” encompasses a single concentration value, as wellas two or more concentration values, and the like, unless implicitly orexplicitly understood or stated otherwise. Further, it will beunderstood that for any given component described herein, any of thepossible candidates or alternatives listed for that component, maygenerally be used individually or in combination with one another,unless implicitly or explicitly understood or stated otherwise.Additionally, it will be understood that any list of such candidates oralternatives, is merely illustrative, not limiting, unless implicitly orexplicitly understood or stated otherwise.

Various terms are described below to facilitate an understanding of thepresent disclosure. It will be understood that a correspondingdescription of these various terms applies to corresponding linguisticor grammatical variations or forms of these various terms. It will alsobe understood that the present disclosure is not limited to theterminology used herein, or the descriptions thereof, for thedescription of particular embodiments. Merely by way of example, thepresent disclosure is not limited to particular analytes, bodily ortissue fluids, blood or capillary blood, or sensor constructs or usages,unless implicitly or explicitly understood or stated otherwise, as suchmay vary. The publications discussed herein are provided solely fortheir disclosure prior to the filing date of the application. Nothingherein is to be construed as an admission that the embodiments of thepresent disclosure are not entitled to antedate such publication byvirtue of prior invention. Further, the dates of publication providedmay be different from the actual publication dates which may need to beindependently confirmed.

As summarized above, in some aspects of the present disclosure, methodsfor analyte monitoring management are provided. The methods includereceiving analyte measurement data and analyzing the analyte measurementdata for health related parameters. The analyte measurement datarepresents analyte measurement data collected over a time period. Themethods also include determining recommendations for creating ormodifying a treatment program based on the analysis. The recommendationsmodulate the health related parameters to improve one or more of thehealth related parameters. The methods include generating auser-interface to enable a user to create or modify the treatmentprogram. The user interface provides the recommendations to guide theuser in creating or modifying the treatment program. The recommendationsare optional and not required to be implemented by the user. The methodsalso include configuring an analyte monitoring device according to thecreated or modified treatment program. In some aspects of the presentdisclosure, articles of manufacture are provided that include amachine-readable medium having machine-executable instructions storedthereon for analyte monitoring management according to the methodsdescribed above.

FIGS. 10-30 illustrate and describe example methods for analytemonitoring management. Moreover, FIGS. 10-30 illustrate the userinterface that is generated to enable a user to create or modify thetreatment program, and further provides the recommendations to guide theuser in creating or modifying the treatment program. The user interfacealso enables the configuring of an analyte monitoring device accordingto the created or modified treatment program, the importing of glucosemeasurement data, and the printing of orders including materials such asreports, summaries, educational material, instructional material, etc.

The user may be, for example, a physician or other health carepractitioner, and the user-interface provided via a data processingdevice, such as a personal computer, a portable computer including alaptop or a handheld device (e.g., a personal digital assistant (PDA), atelephone including a cellular phone (e.g., a multimedia andInternet-enabled mobile phone including an iPhone™, a Blackberry®, orsimilar phone), etc.

In one embodiment, a communication link is established with an analyte(e.g., glucose) monitoring device. The glucose monitoring device may beconnected (wired or wirelessly), for example, to the data processingdevice. For instance, a patient may use the glucose monitoring devicebetween visits to a physician, or other health care practitioner, tocollect glucose measurement data, and then have the glucose monitoringdevice connect either wired or wireless with the data processing device(e.g., computer, laptop, cellular phone, etc.) of the physician duringthe next visit.

The glucose measurements that are received represent data that has beencollected over a time period. Various time periods may be used, such astwo weeks, one month, two months, or any other time period. For example,the time period may reflect the time between visits. In the exampleprovided, the glucose monitoring device is connected to the physician'scomputer and glucose measurement data and the glucose measurement datais received by the physician's computer from the glucose monitoringdevice. In one embodiment, the glucose measurement data may be receivedfrom other devices than the glucose monitoring device—e.g., anotherpersonal computer, portable computer, handheld device, or memory storagedevice, such as Flash memory stick, CD-ROM, etc.

The health related parameter may be, for example, a risk of hypoglycemiaor hyperglycemia, deviation of median glucose with respect to a targetrange, a degree of glucose variability, or any other parameter orindicator of health or area of concern thereof. The risk levelidentifies a level of risk of hypoglycemia based on the collectedmeasurement data. The level of risk of hypoglycemia or hyperglycemia maybe established in various manners—e.g., by looking to hypoglycemic orhyperglycemic episodes, such as the number, duration, timing, or othercharacteristics thereof. Deviations of median glucose may becategorized, for example, as above, below, or within a target range. Thedegree of glucose variability indicates how variable the glucosemeasurements are. Widely varying glucose measurements may presentdifficulties in controlling treatment or parameters. For example, smallvariations in glucose measurements may facilitate more accuratelyestimating the proper dosage, or change in dosage, to improve or controlblood glucose levels.

In some aspects, the analyzing of the analyte measurement data forhealth related parameters includes analyzing the measurement data foranalyte episodes within the collection time period. In one embodiment,the episodes are derived according to the methods described hereinrelating to threshold based episodes. In one embodiment, the episodesare derived according to the methods described herein relating torate-of-change based episodes. In one embodiment, the episodes arederived according to methods described herein relating to both thethreshold based episodes and the rate-of-change based episodes.

The recommendations are provided to modulate the health relatedparameters to improve one or more of the health related parameters. Forexample, the recommendations may relate to lowering glucose variability,adjusting median glucose to be close to the target range, reducing arisk of hypoglycemia or hyperglycemia, etc. The recommendations areoptional and are not required to be implemented by the user.

In one embodiment, a database stores recommendations and correspondingconditions that are associated with or required by the recommendations.For example, a table may include predetermined conditions that areassociated with one or more recommendations. Based on the conditionsthat exist, the appropriate recommendation may be determined. In someinstances, the conditions and requirements may take into accountattributes of the episodes themselves—e.g., type of episode, specificcombinations of episodes present, number of episodes, relation to eventor activities (e.g., meals, fasting periods, exercise, medicationadministration, times of day (e.g., morning, afternoon, night, sleepingperiods, etc.), etc. Based on the resulting attributes of episodes foundfor the measurement data, the associated recommendations in the databasewill be selected.

As stated above, the user-interface is generated to enable the user(e.g., physician or other health care practitioner) to create or modifytreatment. It should be appreciated that the term “user-interface” isused broadly herein. The user interface may be visual and/or audiobased. For example, the user interface may include a graphical userinterface (GUI) generated for display on a display device. It should beappreciated that the user interface may be implemented as a programcontaining one or more GUIs and may include one or more application“screens” or “windows”.

The generated user-interface provides the user with the necessary toolto modify or create a treatment program for the patient. However, therecommendations go further and guide the user in creating or modifyingthe treatment program such that the health related parameters may bemodulated to improve the health related parameter. The recommendationsmay for instance, recommend or suggest that one or more health relatedparameters be targeted for improvement. Furthermore, the recommendationsmay recommend steps necessary to achieve such improvement.

The recommendations may be implemented in a variety of manners withinthe user interface. For example, FIG. 13 illustrates an example GUI thatincludes recommendations resulting from analyzing glucose measurementdata that was collected over a time period. The recommendations A1 areprovided in summary format along with a summary of the health relatedparameters A2 for specific time periods in the day (e.g., during fastingperiods, post-breakfast periods, post-lunch periods, and post-dinnerperiods). A summary A3 of the glucose measurement data over a 24-hourplot is also shown in the GUI.

In one embodiment, the recommendations include a recommendation forcreating or modifying medication parameters of the treatment program.The medication parameters may include, for instance, the medicationselected, the amount of dosage, the frequency or timing ofadministration, etc. For example, it may be determined that a risk ofhypoglycemia is present (e.g., high risk) and recommended thatmedication (e.g., insulin) be increased. The recommendation may alsonote that glucose variability is high and that reducing this variabilitywill allow for greater increases in the medication dose. Therecommendation may further include recommended steps that can be takento assist with the improvement, such as using a greater number ofmeasurements to be scheduled for higher sample count, as shown. In oneembodiment, the recommendation for creating or modifying medicationparameters are provided for different time periods, including timeperiods centered around events such as fasting periods, meal times(e.g., during post-breakfast periods, post-lunch periods, post-dinnerperiods), exercise or other activities, etc.

FIG. 17 illustrates recommendations provided along with the option forthe user to select the corresponding course of action. In the exampleshown, the recommendations are provided for medication adjustmentpurposes. For instance, a summary A10 of the health related parametersare listed for different event periods—e.g., median glucose and glucosevariability are provided fasting periods to indicate that these are theareas of concern determined for this period. The user interface providesthe user with suggested or recommended courses of action A11. Forexample, as shown for fasting, the user is presented with the option ofreducing variability with no medication dose adjustment; or the optionof decreasing the medication adjustment if variability cannot bereduced. The user interface also provides the user with a triggerelement A12 to get more information or further recommendation relatedthereto, as shown by pop up windows A13 on FIG. 18.

In one embodiment, insufficient measurement data is also accounted forand conveyed on the user interface. For example, FIG. 21 illustrates anexample user-interface when insufficient data is encountered. As shown,the recommendation may state that that there is insufficient data toperform an analysis, and may present a warning or suggest a course ofaction to be taken with caution.

In one embodiment, glucose variability is a health related parameter inthe analysis. The user interface may provide recommendations, forexample, in the form of specific questions to ask the patient or todetermine about the patient. For example, FIGS. 23-24 illustrate examplequestions to be answered by the user to assist with treatment creationor modification.

In one embodiment, educational material or other reference material maybe provided to assist or remind the user of the subject matter relatedto the recommendation. For example, guidelines or flowcharts (e.g.,medication administration guidelines) or other pertinent information(e.g., information regarding the medication, such as ingredients, sideeffects, recommended dosages, etc.) may be provided to assist the userwith actions related to the recommendation. For instance, exampleeducational information is shown on FIGS. 17, 18, and 20.

In one embodiment, the user interface enables the user to generate astructured schedule, including setting reminders related to the modifiedor created treatment program. For example, reminders to take measurementreadings may be set and ultimately implemented in the glucose monitoringdevice to remind the patient (or user of the glucose monitoring device)to take a measurement at the appropriate time, to avoid missingmeasurements or taking them too infrequently. Thus, if the treatmentprogram is modified to include a higher number of measurements to betaken, then the reminders may be set accordingly—e.g., to provide areminder for every measurement reading. Other schedules and remindersmay set, such as meal times, applicable start and end dates, etc.

As stated above, the glucose monitoring device is configured accordingto the created or modified treatment program. For example, the glucosemonitoring device may be configured by programming the device with thecreated or modified reminder schedule. For instance, the reminderschedule may be transmitted to the to the glucose monitoring device fromthe physician's computer via the wired or wireless communication link.The configuration data may be stored in the glucose monitoring deviceand thereafter be implemented when the patient begins using the deviceagain. It should be appreciated that many other configuration data maybe included that relate to alerts, medication calculations, metersettings, event reminders, etc.

Furthermore, in one embodiment, a report is generated. Various reportsmay be generated to provide the physician and/or patient with relatedinformation. For example, reports may be generated and then printed.Example information that may be included in a report may include asummary of the glucose measurement data, health related parameters,recommendations, configuration settings, instructions, etc. In oneembodiment, educational material may be generated and printed for thepatient and/or physician. For instance, the educational material mayinclude information on medication, instructions for treatment, self-careinstructions, or educational material geared toward educating thepatient. FIG. 28 illustrates some example material (e.g., summarymaterials and educational materials) that may be generated and providedto the patient.

It should be appreciated that the following embodiments described areexemplary and that other variations may be provided in otherembodiments. It should be appreciated that one or more of the featuresdescribed may be deleted, combined, etc., in other embodiments.Furthermore, while the following embodiments are described with respectto glucose, it should be appreciated that the concepts and principlesapply to analytes in general, and that other analytes (e.g., ketonebodies) may apply in other embodiments.

FIG. 1 illustrates a flowchart of a method for analyte (e.g., glucose)monitoring management, according to one embodiment. At block 105, acommunication link is established with an analyte (e.g., glucose)monitoring device. For instance, a patient may use the glucosemonitoring device between visits to a physician, or other health carepractitioner, to collect glucose measurement data, and then have theglucose monitoring device connect either wired or wireless with thecomputer of the physician during the next visit.

At block 110, glucose measurement data is received. The glucosemeasurement data includes data that has been collected over a timeperiod. Various time periods may be used, such as two weeks, one month,two months, or any other time period. For example, the time period mayreflect the time between visits. In the example provided, the glucosemonitoring device is connected to the physician's computer and glucosemeasurement data and the glucose measurement data is received by thephysician's computer from the glucose monitoring device.

At block 115, the glucose measurement data is analyzed for healthrelated parameters. The health related parameter may be, for example, arisk of hypoglycemia or hyperglycemia, deviation of median glucose withrespect to a target range, a degree of glucose variability, or any otherparameter or indicator of health or area of concern thereof. The risklevel identifies a level of risk of hypoglycemia based on the collectedmeasurement data. The level of risk of hypoglycemia or hyperglycemia maybe established in various manners—e.g., by looking to hypoglycemic orhyperglycemic episodes, such as the number, duration, timing, or othercharacteristics thereof. Deviations of median glucose may becategorized, for example, as above, below, or within a target range. Thedegree of glucose variability indicates how variable the glucosemeasurements are.

In one embodiment, the analyzing of the analyte measurement data forhealth related parameters includes analyzing the measurement data foranalyte episodes within the collection time period. For instance, theepisodes are derived according to the methods described later withrespect to threshold-based episodes and rate-of-change based episodes.

Based on the analysis of the glucose measurement data in block 115,recommendations for creating modifying a treatment program aredetermined, as represented at block 120. The recommendations modulatethe health related parameters to improve one or more of the healthrelated parameters. For example, the recommendations may relate tolowering glucose variability, adjusting median glucose to be close tothe target range, reducing a risk of hypoglycemia, etc. Therecommendations are optional and are not required to be implemented bythe user.

In one embodiment, a database stores recommendations and correspondingconditions that are associated with or required by the recommendations.For example, a table may include predetermined conditions that areassociated with one or more recommendations. Based on the conditionsthat exist, the appropriate recommendation may be determined. In someinstances, the conditions and requirements may take into accountattributes of the episodes themselves—e.g., type of episode, specificcombinations of episodes present, number of episodes, relation to eventor activities (e.g., meals, fasting periods, exercise, medicationadministration, times of day (e.g., morning, afternoon, night, sleepingperiods, etc.), etc. Based on the resulting attributes of episodes foundfor the measurement data, the associated recommendations in the databasewill be selected.

At block 125, a user-interface is generated to enable the user (e.g.,physician or other health care practitioner) to create or modifytreatment. For example, the user interface may include a graphical userinterface (GUI) generated for display on a display device and includesone or more application “screens” or “windows”.

The generated user-interface provides the user with the necessary toolto modify or create a treatment program for the patient. However, therecommendations go further and guide the user in creating or modifyingthe treatment program such that the health related parameters may bemodulated to improve the health related parameter. The previousdiscussion and examples for recommendations apply here as well.

At block 130, the glucose monitoring device is configured according tothe created or modified treatment program. For example, the glucosemonitoring device may be configured by programming the device with thecreated or modified reminder schedule. For instance, the reminderschedule may be transmitted to the to the glucose monitoring device fromthe physician's computer via the wired or wireless communication link.The configuration data may be stored in the glucose monitoring deviceand thereafter be implemented when the patient begins using the deviceagain.

At block 135, a report is generated. Various reports may be generated toprovide the physician and/or patient with related information. Forexample, reports may be generated and then printed. At block 140, anypatient education material is generated to be provided (e.g., printed ortransferred to the glucose monitoring device) for viewing at a latertime.

Threshold Based Episode

As summarized above, in some aspects of the present disclosure, methodsfor managing analyte measurement data are provided. The methods includereceiving analyte measurement data that represent data collected over atime period, and analyzing the analyte measurement data for analyteepisodes within that time period. The analyte episodes include at leastone threshold based episode. The threshold based episode is based onmeasurements meeting an entrance threshold for entering the thresholdbased episode. Further, the threshold based episode requires at leastone of: a minimum number of measurements meeting the entrance threshold;a minimum duration of time meeting the entrance threshold; and a minimumarea for measurements meeting the entrance threshold. The methods alsoinclude storing the analyte episodes in memory. In some aspects of thepresent disclosure, articles of manufacture are provided that include amachine-readable medium having machine-executable instructions storedthereon for managing analyte measurement data according to the methodsdescribed above.

In practical applications, analyte measurement data may provide problemsfor episode detection due to various factors. For example, gaps inmeasurement data (e.g., missing measurements) may present problems indetermining if the gap exists in one episode or exists between twoepisodes. Furthermore, outliers or other brief measurement data pointsmay provide problems by improperly crossing value thresholds (e.g.,entrance thresholds or exit thresholds) and rate of change basedthresholds. Noise may present similar issues.

It should be appreciated that rate-of-change based thresholds refer tothresholds of rate-of-change (also referred to herein “rate thresholds”or “threshold rate”). The term “value thresholds” is used hereingenerally to distinguish the thresholds from thresholds forrate-of-changes. Such value thresholds may include, for example,entrance thresholds, exit thresholds, thresholds of duration (alsoreferred to herein as “duration thresholds”), thresholds for minimumnumber of measurements, thresholds for minimum area, etc. Valuethresholds may also be referred to herein simply as thresholds in someinstances.

In some aspects, methods are provided that resolve the above-mentionedissues and problems in episode detection. For example, these methods maybe used to search glucose measurement values to detect extreme episodesof clinical interest. Therefore, the episode may be more clinicallymeaningful. In addition, the present disclosure specifies the propertiesof episodes that can be clinically meaningful. These properties can alsobe used to construct sequences or “chains” of episodes that havespecific clinical meaning related to self-care behaviors.

The core logic of episode analysis falls into two families: thresholdbased, and rate-of-change based thresholds. Looking for episodes in bothdirections, for example, suggests four basic episode types: lowglucose/hypoglycemia (measurements below a threshold); highglucose/hyperglycemia (measurements above a threshold); glucose fall(rate-of-change more negative than a negative threshold rate); andglucose rise (rate-of-change more positive than a positive thresholdrate). In one embodiment, a “within target” episode is defined toidentify an episode where the measurements are maintained between anupper and lower bound for a period of time. Detection of these episodescan be done by extension of the threshold-based episode detectionalgorithms.

Grouping all consecutive measurements (e.g., above or below) a thresholdto form an episode creates issues and challenges in practicalapplications. For example, very brief episodes or outlier values mayskew the identification of episodes by being improperly identified as anepisode.

In one embodiment, the methods for managing analyte measurement datadescribed herein requires threshold based episodes to be based onmeasurements meeting an entrance threshold. Further, the threshold basedepisode requires one or more of the following: a minimum number ofmeasurements meeting the entrance threshold; a minimum duration of timemeeting the entrance threshold; and a minimum area for measurementsmeeting the entrance threshold. In one embodiment, all three criteriamay be required. In one embodiment, all three are required to have anepisode.

It should be appreciated that “meeting” a threshold is used herein tomean that the threshold is triggered. It should be appreciated that“meeting a threshold” is used generally herein to refer to instanceswhere a threshold is triggered by values “equal to” or “exceeding” thethreshold; as well as to other instances where a threshold is triggeredby values only “exceeding” the threshold. Furthermore, it should beappreciated that the phrase “exceeding a threshold” is used hereingenerally to refer to values that are beyond the threshold such that thethreshold is triggered, whether the threshold is triggered bymeasurements above the threshold (e.g., hyperglycemic episodes) or belowthe threshold value (hypoglycemic episodes). This applies to both valuebased thresholds and rate-of-change thresholds. For example, if ameasurement is greater than a threshold for hyperglycemia, then thethreshold is exceeded and the threshold is met. Similarly, if ameasurement is less than a threshold for hypoglycemia, then thethreshold is exceeded and the threshold is met.

Gaps in measurement data may also provide problems by significantlyaltering an episode duration. In one embodiment, a threshold basedepisode is defined based on a gap threshold such that a single episodeis maintained at any gaps shorter than the gap threshold, and split intotwo separate potential episodes at any gaps meeting the gap threshold.The potential episodes are determined to be episodes if all othercriteria are met.

Noise may also present problems in defining episodes, especially whenthe true value is near a threshold. For example, noise may cause manyepisodes to be recorded when the true value is close to the threshold.In one embodiment, a threshold based episode is defined based on an exitthreshold for exiting the episode, such that the at least one thresholdbased episode ends when measurements meet the exit threshold. In oneembodiment, the exit threshold is a value “outside” the entrancethreshold. The term “outside” is used here to mean that the exitthreshold does not “equal” the entrance threshold or “exceed” theentrance threshold such that it would trigger the entrance threshold.Therefore, to exit an episode, measurements within the episode mustfirst reach and pass the entrance threshold before eventually reachingthe exit threshold. In this way, measurement will be “debounced” suchthat the episode is only terminated following a threshold crossing ifthe signal also crosses the exit threshold.

Properties of threshold based episodes may be defined for clinicalutility, including but not limited to: threshold value, most extremevalue (magnitude of excursion past threshold), episode duration, orepisode area. It should be appreciated that many other episode types maybe provided, each of which, if independently clinically relevant, couldform the basis for reports and analysis.

The following is an example pseudocode implementation of athreshold-based episode detection algorithm, according to oneembodiment:

//″State″ is the previous condition, “PointState” is the condition forthe new point void BuildList( ) { EpisodeState State = NotInEpisode; ForEach CGMValue In Database EpisodeState PointState =GetEpisodeState(CGMValue) if (PointState == InEpisode) { if (Gap fromprevious point >= maximum gap) { //end of possible episode //if itpasses all checks... if (ReadingsInEpisode >= MinimumReadings &&EpisodeDuration >= MinimumDuration && EpisodeArea >= MinimumArea) {//add it to the list of episodes } //Start of posssible episode //recordstart time, reset point count and cumulative area } if (State ==NotInEpisode) //Start of possible episode //record start time, resetpoint count and cumulative area } else if (State == InEpisode) {//continuation of possible episode //push back end time, increment pointcount, add to cumulative area } else // if (State == BetweenThresholds){ //debounce region } State = InEpisode; } else if (PointState ==NotInEpisode) { if (State == BetweenThresholds || State == InEpisode) {//end of possible episode //if it passes all checks... if(ReadingsInEpisode >= MinimumReadings && EpisodeDuration >=MinimumDuration && EpisodeArea >= MinimumArea) { //add it to the list ofepisodes } } State == NotInEpisode; } Next CGMValue }

FIG. 2A illustrates a flowchart for a method of managing analytemeasurement data, according to one embodiment. As shown at block 205,analyte (e.g., glucose) measurement data is received by a dataprocessing device, such as a glucose monitoring device, personalcomputer, a portable computer including a laptop or a handheld device(e.g., a personal digital assistant (PDA), a telephone including acellular phone (e.g., a multimedia and Internet-enabled mobile phoneincluding an iPhone™, a Blackberry®, or similar phone), etc. The analytemeasurement data represents analyte measurements collected over a periodof time. Various time periods may be used, such as two weeks, one month,two months, or any other time period. For example, the time period mayreflect the time between visits. The data processing device may receivethe analyte measurement data from a glucose monitoring device or otherdevice—e.g., another personal computer, portable computer, handhelddevice, or memory storage device, such as Flash memory stick, CD-ROM,etc.

At block 210, the analyte measurement data is analyzed for analyteepisodes that are threshold based episodes. The threshold based episodeis based on measurements meeting an entrance threshold for entering theepisode. Furthermore, the threshold based episode requires at least oneof: a minimum number of measurements meeting the entrance threshold; aminimum duration of time meeting the entrance threshold; and a minimumarea for measurements meeting the entrance threshold. As later discussedand illustrated in FIG. 4, the area associated with the measurementsmeeting the entrance threshold is defined as the area on a glucoseversus time plot of measurement that is between the measurements meetingthe entrance threshold and the entrance threshold itself. At block 215,any threshold based episodes are stored in memory for further datamanagement.

FIG. 2B illustrates a flowchart for a method of managing analytemeasurement data wherein the analyte measurement data is analyzed foranalyte episodes that are rate of change based episodes. This isdiscussed in further detail later in the section for rate-of-changebased episodes. In other embodiments, methods of managing analytemeasurement data may include analyzing the measurement data for boththreshold based episodes and rate-of-change based episodes.

FIG. 3 illustrates a flowchart of a method for managing analyte (e.g.,glucose) measurements that includes analyzing for threshold basedepisodes, according to one embodiment. The glucose measurement data isreceived and analyzed for threshold based episodes. The analysis maybegin, for example, at the earliest measurement and continued throughtill the latest measurement.

Beginning at block 305, the previous glucose measurement value is set to“not in an episode” at the start of the analysis. At block 310, the“newest” or next glucose value (measurement) in time-ordered series isselected for analysis. If the next glucose value is the last value ofthe glucose measurement data, then the process is ended, as shown byblock 315. If the next glucose value is not the last measurement, thenthe state of that glucose measurement is checked, as shown in block 320.If it is determined that the next glucose value is between thresholds,then the next glucose value in time-ordered series is selected at block310. “Between episodes” refers to the being between the entrancethreshold and exit threshold.

If it is determined that the measurement is not in episode, then thestate of the previous glucose value is checked, as shown in block 325.Then it is determined if the previous measurements is in episode orbetween thresholds. For example, in the embodiment shown, at block 330,the episode requires: 1) minimum number of measurements meeting theentrance threshold; 2) a minimum duration of time meeting the entrancethreshold; and 3) a minimum area for measurements meeting the entrancethreshold (e.g., the area under or above the glucose v. time curverelative to entrance threshold for a hypoglycemic or hyperglycemicepisode, respectively). Since all three criteria are required in thisembodiment, if any one of the criteria is not met, then as shown atblock 335, an episode does not exist and is not added to the searchresults (e.g., not stored in memory and used for further glucosemeasurement management). If all three criteria are met, as shown inblock 340, then the episode exists and is added to the list of episodes(e.g., stored in memory and used for further glucose measurementmanagement). The state of the previous glucose value is then set to “notin episode” so that episodes found in the subsequent data are analyzedindependently of the previously identified episode.

Returning to block 320, if the next glucose value is determine to be inan episode, then the gap to the previous point is checked against apredetermined gap threshold, as shown in block 345. If the gap to theprevious point is smaller than the gap threshold, then the state of theprevious glucose value is checked, as shown in block 350. If itdetermined that the previous glucose value is in episode or betweenthresholds, then the state of the previous glucose value is continued inepisode, as shown at block 355. Further at block 355, variousproperties, such as end time, point count and “exposure” or area arestored. Then a next glucose value in time-ordered series is selected, asshown at block 310. If it is determined at block 350 that the previousglucose value is not in episode, then the previous glucose value is setto in episode, and properties such as start time, point count andexposure (area) are stored, as shown in block 360. Then the next glucosevalue in time-ordered series is selected, as shown in block 310.

Returning to block 345, if the gap to the previous point meets the gapthreshold (e.g., is larger than the gap threshold, or in otherembodiments is equal to or larger), then it is determined at block 365if the three criteria are met for the episode: 1) minimum number ofmeasurements meeting the entrance threshold (e.g., minimum number ofmeasurement readings after the entrance threshold was crossed); 2) aminimum duration of time meeting the entrance threshold; and 3) aminimum area for measurements meeting the entrance threshold (e.g., thearea under or above the glucose v. time curve relative to entrancethreshold). If any one of the criteria is not met, then as shown atblock 370, the episode does not exist and is not added to the searchresults (e.g., stored in memory and used for further glucose measurementmanagement). If all three criteria are met, as shown in block 375, thenthe episode exists and is added to the list of episodes (e.g., stored inmemory and used for further glucose measurement management). The stateof the previous glucose value is then set to “not in episode”.

After blocks 370 and 375, the previous glucose value is set to inepisode and the properties, such as start time, point count and exposure(area) are stored, as shown in block 360. Then the next glucose value intime-ordered series is selected, as shown in block 310.

FIG. 4 illustrates a hypoglycemic episode for a set of glucosemeasurement data, according to one embodiment. As shown, glucosemeasurements 405 are plotted on a chart of glucose versus time. Theentrance threshold 410 is shown for a hypoglycemia event. In thishypoglycemic example, the measurements that meet the entrance thresholdare measurements that reach or exceed the entrance threshold by goingbelow the entrance threshold (e.g., are smaller in value). As shown,measurement 405A is above the entrance threshold and thus not meetingthe initial threshold for entering a hypoglycemic episode. Measurement405B is the next measurement and “exceeds” the entrance threshold 410for entering the hypoglycemic episode, and thus meets the entrancethreshold. The next four measurements 405E are below the exit threshold415. Measurement 405C is above the exit threshold 415 and thus “exceeds”and meets the exit threshold 415. In the embodiment shown, the threecriteria are met for an episode: 1) minimum number of measurementsoutside the entrance threshold (e.g., the minimum number of measurementsmay be three measurements); 2) a minimum duration of time outside theentrance threshold (e.g., the minimum duration of time may be 20minutes); and 3) a minimum area outside the entrance threshold (e.g.,the area between the entrance threshold and the measurements that meetthe entrance threshold).

Therefore, measurement 405B and 405C are the start and end of theepisode, respectively, and the duration 420 of the episode spanning thetime between the two. Therefore, the episode duration 420 is shown withthe pre-event window 425 prior and post-event window 430 thereafter.Measurement 405D represents the lowest measurement in the episode.

FIG. 5 illustrates a hyperglycemic episode for a set of glucosemeasurement data, according to one embodiment. As shown, glucosemeasurements 505 are plotted on a chart of glucose versus time. Theentrance threshold 510 is shown for a hyperglycemia event. As shown,measurement 505A has not reached or exceeded (e.g., is below) theentrance threshold and thus not meeting the initial thresholdrequirement of entering a hyperglycemic episode. Measurement 505B is thenext measurement and the entrance threshold 510 is met (e.g., reached orexceeded), and thus the first requirement of entering a hyperglycemicepisode is met. Each consecutive measurement thereafter does not crossthe exit threshold 515 until measurement 505E. However, the nextmeasurement 505F returns back above the exit threshold and due to theduration threshold requirement, the measurements did not stay past theexit threshold for a required duration threshold (e.g., assuming anexample duration threshold of 20 minutes). Thus the episode continuesand does not end. Measurement 505C crosses the exit threshold and thenext 3 measurements stay past the exit threshold (e.g., stay past theexit threshold for 30 minutes, which is longer than the example durationthreshold of 20 minutes), thus meeting the duration thresholdrequirement. Therefore, the episode begins at measurement 505B and endsat measurement 505C.

The episode duration 520 is shown with the pre-event window 525 priorand post-event window 530 thereafter. The episode includes a maximummeasurement 505D in the episode.

Rate-of-Change Based Episodes

As summarized above, in some aspects of the present disclosure, methodsfor managing analyte measurement data are provided. The methods includereceiving analyte measurement data that represent data collected over atime period, and analyzing the analyte measurement data for analyteepisodes within that time period. The analyte episodes include at leastone rate-of-change based episode. The rate-of-change based episoderequires a core of the episode to meet a threshold rate for a durationthreshold. The methods also include storing the analyte episodes inmemory. In some aspects of the present disclosure, articles ofmanufacture are provided that include a machine-readable medium havingmachine-executable instructions stored thereon for managing analytemeasurement data according to the methods described above.

Grouping all consecutive monotonically increasing/decreasing points toform an episode creates issues and challenges in practical applications.For instance, small changes in magnitude may not be meaningful.Furthermore, signal variation may also present issues and problems byexaggerating the rate of change of very brief episodes. In oneembodiment, the rate-of-change based episode requires a core of theepisode to meet a threshold rate. The core is formed by two pointshaving a threshold core rate-of-change for a duration threshold. Localextrema may then be scanned outward from the core to potentially definethe episode.

Gaps in the measurement data can also significantly alter the episodedurations. In one embodiment, a rate-of-change based episode is definedbased on a gap threshold such that a single episode at any gaps shorterthan the gap threshold, and split into two separate potential episodesat any gaps meeting the gap threshold. All of the points before the gapare considered a potentially complete episode with the last point beingthe point preceding the gap. All the points after the gap form the startof a potentially new episode. Again, the potential episodes aredetermined to be episodes if all other episode criteria are met.

Noise presents issues and problems by breaking the monotonicity of thechange during periods of relatively slow change. In one embodiment, therate-of-change based episode is defined based on a distance thresholdbetween episodes such that two episodes within the distance threshold ofeach other are merged into a single episode. Thus, episodes that areclose together are merged into a single episode, resulting in a newlydefined episode containing all of the points between the first point ofthe first episode and the last point of the second episode.

In some instances, episodes merged in this way could have intermediateextreme points outside of the end values. In one embodiment, the singleepisode of the merger has intermediate extreme points outside of endvalues of the single episode, and wherein the single episode is thenredefined beginning and ending at the intermediate extreme points.Episodes redefined in this way could include spikes caused by twoclosely spaced points where one of which is an outlier. The criteriarequiring a duration threshold may be implemented in other embodimentsto alleviate such issue.

Properties of change episodes, as so defined, can be defined, includingbut not limited to: maximum rate, delta (highest-lowest values), lowestvalue, and highest value. This provides a virtually limitless catalog ofepisode types, each of which, if independently clinically relevant,could form the basis for reports and analysis.

The following is an example pseudocode implementation of arate-of-change based episode detection algorithm, according to oneembodiment:

void Buildlist( ) { For Each FirstValue In Database For Each NextValueIn Database (Starting at FirstValue) if (Distance between NextValue andpoint before it > MaxGap) { if (Last Episode passes checks) { //log lastepisode } FirstValue = NextValue Next FirstValue } else if(GetRateOfChange (FirstValue, NextValue) > Threshold) { StartingValue =ScanBackForLocalExtrema (FirstValue) ; EndingValue =ScanForwardFbrLpcalExtrema (NextValue) ; HighestValue =FindMaxBetween(StartingValue, EndingValue); LowestValue = FindMinBetween(StartingValue, EndingValue) ; StartingValue = (HighestValue orLowestValue); EndingValue = (LowestValue or HighestValue) ; if(StartingValue is close enough to EndingValue of last episode) { //merge with last episode } else {  if (Last Episode passes checks)  {//log last episode  }  //store this episode as last episode for nextpass } } Next NextValue Next FirstValue

As stated above, FIG. 2B illustrates a flowchart of a method formanaging analyte (e.g., glucose) measurements that includes analyzingfor rate-of-change based episodes. As shown at block 250, similar toblock 205 in FIG. 2A, analyte (e.g., glucose) measurement data isreceived. At block 255, however, the analyte measurement data isanalyzed for analyte episodes that are rate of change based episodes.The rate of change based episode requires a core of the episode to meeta threshold rate. The core is formed by two points having a thresholdrate-of-change for a threshold duration of time (duration threshold). Atblock 260, any threshold based episodes are stored in memory for furtherdata management.

FIG. 6 illustrates a flowchart of a method for managing analyte (e.g.,glucose) measurements, according to one embodiment. The glucosemeasurement data is received and analyzed for rate-of-change basedepisodes. The analysis may begin, for example, at the earliestmeasurement and continued through till the latest measurement.

At block 605, for each point in the data set, a first value(measurement) is identified. At block 610, each point after the firstvalue, referred to here as the next value, is identified and thedistance between the next value and the first point is determined andcompared to a gap threshold (e.g., a maximum gap), as shown in block615. If the distance is not greater than the gap threshold, then it isdetermined if the rate of change between the first value and the nextexceeds the minimum threshold rate, as represented at block 620. If itdoes not meet the threshold rate (e.g., does not exceed the thresholdrate), then another ‘next value’ is selected (e.g., the next measurementin time-ordered series) for analysis, as shown by block 610.

If at block 615, the distance is greater than the gap threshold, then itis determined if the last episode is longer than a duration threshold,as shown by block 625. If it is not longer than the duration threshold,then the first value is set to the next value, to prevent reanalyzingthe same points on the next pass, as represented by block 630 and arrowreturning to block 605. If the last episode is longer than the durationthreshold, then an episode is identified and stored in the episode list(e.g., in memory), as shown in block 635. Then the first value is set tothe next value, to prevent reanalyzing the same points on the next pass,as represented by block 630 and arrow returning to block 605.

Returning to block 620, if the rate of change between the first valueand the next value exceeds the minimum, then local extrema (e.g., firstpoint and last point) are determined by scanning back form the firstvalue and forward from the next value, as shown at block 640. Then theminimum and maximum (e.g., highest value and lowest value) aredetermined by scanning from the first point to the last point, as shownby block 645. The starting value and ending value are set to the highestvalue and the lowest value (or vice versa).

Then it is determined if the difference between the ending value of theprevious episode and the starting value is less than a predeterminedacceptable minimum (e.g., a distance threshold between the twoepisodes). If so, then the two episodes are combined, as shown by block655. The first point is equal to the starting value of the last episode,and the last point is the ending value of this episode. The previousepisode is eliminated. Then to block 645, where the minimum and maximum(e.g., highest value and lowest value) are determined by scanning fromthe first point to the last point. The starting value and ending valueare set to the highest value and the lowest value (or vice versa).

If at block 650, the difference between the ending value of the previousepisode and the starting value is not less than a predeterminedacceptable minimum, then this episode is stored as the last episode forthe next pass, as shown by block 660. Then again back to block 625 whereit is determined if the last episode is longer than a durationthreshold.

FIG. 7 illustrates an example of a detection of a glucose rise episodein a set of glucose measurement data, according to one embodiment. Asshown glucose measurement data 705 is plotted on a chart of glucoseversus time. Starting with the earliest measurements on the left, eachmeasurement is compared with future measurements to determine if apredetermined rate threshold is met (e.g., reached or exceeded). Forexample, in the example shown, measurement 705A and prior measurementsto the left, do not have any future measurements that create arate-of-change above a predetermined threshold rate. However,measurement 705B and future measurement 705F embody a rate-of-changeabove the predetermined threshold rate. For example, the top of triggerwindow 710 represents the predetermined threshold rate and is shown formeasurement 705B. As shown, measurement 705F is above the top of triggerwindow 710, and thus possesses a rate-of-change greater than thepredetermined rate threshold of the trigger window 710. A core is thusdefined from measurement 705B as the trigger start point to measurement705F as the trigger end point. Note that for all the measurements priorto measurement 705B, no future measurements exist that would fall abovethe top of similarly drawn trigger windows (not shown) which align withthe corresponding measurement being analyzed.

Local extrema are then scanned for outside the core. For example,scanning from measurement 705B to the left, a minimum point is scannedfor. Measurement 705C is determined to be the minimum and thus theepisode start point. Similarly, scanning from measurement 705F to theright, a maximum point is scanned for. Measurement 705G is determined tobe the maximum and thus the episode end point. Note that the episodeincludes a minimum and maximum point for the episode itself, which mayor may not be the same as the local extrema searched for. In this case,the minimum measurement 705H is the episode's minimum while measurement705G is the episode's maximum. The episode duration 720 is shown withthe pre-event window 725 prior and post-event window 730 thereafter. Theprocess may then be repeated for later measurements past the episode tofind additional rate-of-change based episodes within the measurementdata.

FIG. 8 illustrates an example of a detection of a glucose fall episodein a set of glucose measurement data, according to one embodiment. Asshown glucose measurement data 805 is plotted on a chart of glucoseversus time. Starting with the earliest measurements on the left, eachmeasurement is compared with future measurements to determine if apredetermined rate threshold is met (e.g., reached or exceeded). Forexample, in the example shown, measurement 805A and prior measurementsto the left, do not have any future measurements that create arate-of-change above a predetermined threshold rate. However,measurement 805B and future measurement 805F possess a rate-of-changeexceeding the predetermined threshold rate. For example, the bottom oftrigger window 810 represents the predetermined threshold rate and isshown for measurement 805B. As shown, measurement 805F is below thebottom of trigger window 810, and thus possesses a rate-of-changeexceeding than the predetermined rate threshold of the trigger window810. A core is thus defined from measurement 805B as the trigger startpoint to measurement 805F as the trigger end point. Note that for allthe measurements prior to measurement 805B, no future measurements existthat would fall below the bottom of similarly drawn trigger windows (notshown) which align with the corresponding measurement being analyzed.

Local extrema are then scanned for outside the core. For example,scanning from measurement 805B to the left, a maximum point is scannedfor. Measurement 805B is determined to be the minimum and thus theepisode start point. Similarly, scanning from measurement 805F to theright, a minimum point is scanned for. Measurement 805G is determined tobe the minimum and thus the episode end point. Note that the episodeincludes a minimum and maximum point for the episode itself, which mayor may not be the same as the local extrema searched for. In this case,the minimum measurement 805G is the episode's minimum while measurement805H is the episode's maximum. The episode duration 820 is shown withthe pre-event window 825 prior and post-event window 830 thereafter. Theprocess may then be repeated for later measurements past the episode tofind additional rate-of-change based episodes within the measurementdata.

Devices and Systems

FIG. 9 illustrates a data processing device that may perform the methodsdescribed herein, according to one embodiment. The data processingdevice 900 is shown including processor 910, communication unit 923,memory 915, display unit 921 and input/output 920. The data processingdevice may communicate, either wired or wireless, with other devices,such as a medication delivery device 905, portable processing device906, computer 907, or an analyte monitoring device 910. The dataprocessing device may also be coupled to networks 908 and/or theinternet 909. The analyte monitoring device and/or system 910 may, forexample, provide for discrete monitoring of one or more analytes usingan in vitro blood glucose (“BG”) meter and an analyte test strip. Inother embodiments, the glucose monitoring device may provide forcontinuous, periodic, and/or intermittent in vivo monitoring of thelevel of one or more analytes. For instance, such a system may include,for example, an analyte sensor at least a portion of which is to bepositioned beneath a skin surface of a user for a period of time.

In one embodiment, data processing device 900 may be a computer of aphysician or HCP that connects to glucose monitoring device 910 of apatient. The computer 900 may then communicate data to and from othercomputers 907 and the internet 909. In another embodiment, the dataprocessing device 900 may be a handheld device, such as a cellular phoneor hand-held computer, that connects with the glucose monitoring device910 of a patient. The device 900 may then communicate data to and from apersonal computers 907, for example, via a direct wired or wirelessconnection, or via the internet 909.

In one embodiment, instructions for performing the methods describedherein may be stored in memory unit 915 and executed by processor 910.The communication unit 923 may be used to establish a communication linkbetween the analyte monitoring device 910 and the data processing device900. Display unit 921 and input/output 920 may be used to provide theuser interface and receive user input. Input/output 920 may also be usedto connect to a printer to print reports.

Each of the various references, presentations, publications, provisionaland/or non-provisional U.S. patent applications, U.S. patents, non-U.S.patent applications, and/or non-U.S. patents that have been identifiedherein, is incorporated herein by reference in its entirety.

Other embodiments and modifications within the scope of the presentdisclosure will be apparent to those skilled in the relevant art.Various modifications, processes, as well as numerous structures towhich the embodiments of the present disclosure may be applicable willbe readily apparent to those of skill in the art to which the presentdisclosure is directed upon review of the specification. Various aspectsand features of the present disclosure may have been explained ordescribed in relation to understandings, beliefs, theories, underlyingassumptions, and/or working or prophetic examples, although it will beunderstood that the present disclosure is not bound to any particularunderstanding, belief, theory, underlying assumption, and/or working orprophetic example. Although various aspects and features of the presentdisclosure may have been described largely with respect to applications,or more specifically, medical applications, involving diabetic humans,it will be understood that such aspects and features also relate to anyof a variety of applications involving non-diabetic humans and any andall other animals. Further, although various aspects and features of thepresent disclosure may have been described largely with respect toapplications involving partially implanted sensors, such astranscutaneous or subcutaneous sensors, it will be understood that suchaspects and features also relate to any of a variety of sensors that aresuitable for use in connection with the body of an animal or a human,such as those suitable for use as fully implanted in the body of ananimal or a human. Finally, although the various aspects and features ofthe present disclosure have been described with respect to variousembodiments and specific examples herein, all of which may be made orcarried out conventionally, it will be understood that the invention isentitled to protection within the full scope of the appended claims.

It should be understood that techniques introduced above can beimplemented by programmable circuitry programmed or configured bysoftware and/or firmware, or they can be implemented entirely byspecial-purpose “hardwired” circuitry, or in a combination of suchforms. Such special-purpose circuitry (if any) can be in the form of,for example, one or more application-specific integrated circuits(ASICS), programmable logic devices (PLDs), field-programmable gatearrays (FPGAs), etc.

Software or firmware implementing the techniques introduced herein maybe stored on a machine-readable storage medium and may be executed byone or more general-purpose or special-purpose programmablemicroprocessors. A “machine-readable medium”, as the term is usedherein, includes any mechanism that can store information in a formaccessible by a machine (a machine may be, for example, a computer,network device, cellular phone, personal digital assistant (PDA),manufacturing took, any device with one or more processors, etc.). Forexample, a machine-accessible medium includes recordable/non-recordablemedia (e.g., read-only memory (ROM); random access memory (RAM);magnetic disk storage media; optical storage media; flash memorydevices; etc.), etc.

The following examples are put forth so as to provide those of ordinaryskill in the art with a complete disclosure and description of how tomake and use the embodiments of the invention, and are not intended tolimit the scope of what the inventors regard as their invention nor arethey intended to represent that the experiments below are all or theonly experiments performed. Efforts have been made to ensure accuracywith respect to numbers used (e.g., amounts, temperature, etc.) but someexperimental errors and deviations should be accounted for. Unlessindicated otherwise, parts are parts by weight, molecular weight isweight average molecular weight, temperature is in degrees Centigrade,and pressure is at or near atmospheric.

What is claimed is:
 1. A method of managing analyte measurement datawith a data processor, comprising: receiving analyte measurement data,the analyte measurement data representing analyte measurement datacollected over a time period; analyzing the analyte measurement data toidentify at least one rate-of-change based analyte episode within thetime period, the rate-of-change being the rate of change of an analytevalue over time, wherein the at least one rate-of-change based episodecomprises a core that extends between a first and an end analyte value,wherein the at least one rate-of-change episode has a threshold corerate-of-change for a duration threshold; identifying a first localextreme analyte value that occurs before the first analyte value and asecond local extreme analyte value that occurs after the end analytevalue; identifying a lowest and a highest analyte value between thefirst and second local extreme analyte values; and defining a beginninganalyte value of the at least one rate-of-change based episode to be oneof the lowest or highest analyte values and an ending analyte value ofthe at least one rate-of-change based episode to be the other of thelowest or highest analyte values.
 2. The method of claim 1, wherein theat least one rate-of-change based episode is identified based on a gapthreshold such that a single episode is identified at gaps shorter thanthe gap threshold, and two separate potential episodes are identified atgaps meeting the gap threshold, wherein the gap threshold is a maximumpredetermined difference in time between a first analyte value and anext analyte value.
 3. The method of 1, wherein the at least onerate-of-change based episode is identified based on a distance thresholdbetween episodes such that first and second episodes within the distancethreshold of each other are merged into a single episode, wherein thedistance threshold is a minimum predetermined difference in time betweenthe ending analyte value of the first episode and the beginning analytevalue of the second episode.
 4. The method of claim 1, wherein theanalyte is glucose or a ketone body.
 5. The method of claim 1, furthercomprising the step of storing the at least one rate-of-change basedanalyte episode in memory.
 6. A data processing device, comprising: aprocessor; a memory device having a plurality of instructions storedthereon that, when executed, cause the processor to: analyze analytemeasurement data to identify at least one rate-of-change based analyteepisode within a time period, the analyte measurement data representinganalyte measurement data collected over a time period, therate-of-change being the rate of change of an analyte value over time,wherein the at least one rate-of-change based episode comprises a corethat extends between a first and an end analyte value, wherein the atleast one rate-of-change episode has a threshold core rate-of-change fora duration threshold; identify a first local extreme analyte value thatoccurs before the first analyte value and a second local extreme analytevalue that occurs after the end analyte value; identify a lowest and ahighest analyte value between the first and second local extreme analytevalues; and define a beginning analyte value of the at least onerate-of-change based episode to be one of the lowest or highest analytevalues and an ending analyte value of the at least one rate-of-changebased episode to be the other of the lowest or highest analyte values.7. The data processing device of claim 6, wherein the at least onerate-of-change based episode is identified based on a gap threshold,wherein the plurality of instructions, when executed, cause theprocessor to: identify a single episode at gaps shorter than the gapthreshold, and identify two separate potential episodes at gaps meetingthe gap threshold, wherein the gap threshold is a maximum predetermineddifference in time between a first analyte value and a next analytevalue.
 8. The data processing device of claim 6, wherein at least onerate-of-change based episode is defined identified based on a distancethreshold between episodes, wherein the plurality of instructions, whenexecuted, cause the processor to merge first and second episodes withinthe distance threshold of each other into a single episode, wherein thedistance threshold is a minimum predetermined difference in time betweenan ending analyte value of the first episode and a beginning analytevalue of the second episode.
 9. The data processing device of claim 6,wherein the analyte is glucose.
 10. The data processing device of claim6, wherein the plurality of instructions, when executed, further causethe processor to store the analyte episodes in memory.